Deepak Ingole

About

Deepak Ingole is a researcher at the forefront of embedded control systems, with a focus on bridging the gap between advanced control theory and real-time hardware implementation. His work centers on nonlinear model predictive control (NMPC), a powerful but computationally demanding technique, and he has pioneered innovative approaches to make it viable on resource-limited platforms. In his highly cited 2022 paper, "FPGA Implementation of Low Complexity Nonlinear Model Predictive Control Using Deep Learning Approach," Ingole tackles the core bottleneck of NMPC: solving complex optimization problems within strict sample times on embedded hardware. By integrating deep learning to approximate the control law, he demonstrates a path to efficient, real-time execution on FPGAs, opening doors for applications in autonomous systems, robotics, and industrial automation. With over 2 citations already, this work signals growing recognition of his contributions to making advanced control accessible for practical, high-speed environments. Ingole’s research is essential reading for students and engineers seeking to understand how machine learning can unlock the potential of model predictive control in the physical world.

Research Focus

Key Achievements

Top Papers

1. 1
   FPGA Implementation of Low Complexity Nonlinear Model Predictive Control Using Deep Learning Approach

   2 citations · 2022

Key Collaborators

Contact & Links